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reporting-and-archiving-findings

Use when an analysis is complete and verified, and you need to decide how to report it and archive the work for reproducibility

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Reporting and Archiving Findings

Overview

Complete an investigation by confirming reproducibility, presenting clear options, handling the chosen one, and archiving everything needed to reproduce the result.

Core principle: Confirm reproducibility -> separate confirmatory from exploratory -> present options -> execute choice -> archive code + data + environment + pre-registration.

Announce at start: "I'm using the reporting-and-archiving-findings skill to complete this work."

Step 1: Verify Reproducibility

Before reporting anything, confirm the whole analysis reproduces from immutable raw data with the fixed seed in the pinned environment.

# From a clean state: re-run the pipeline end to end
# Confirm headline numbers match what you intend to report

Use science-superpowers:verifying-results-before-claiming. If it doesn't reproduce, stop — fix reproducibility (possibly via science-superpowers:investigating-anomalous-results) before reporting. Don't report a number you can't regenerate.

For any confirmatory claim, also run the pre-registration audit and keep its output for the report:

# ships with science-superpowers:preregistering-analysis
<skills root>/preregistering-analysis/prereg.sh audit

If the audit fails, the confirmatory label is indefensible — the registration changed after its freeze, or outputs predate it. Relabel the affected analyses exploratory (or pre-register a fresh test on unused data) before reporting.

Step 2: Detect Environment

GIT_DIR=$(cd "$(git rev-parse --git-dir)" 2>/dev/null && pwd -P)
GIT_COMMON=$(cd "$(git rev-parse --git-common-dir)" 2>/dev/null && pwd -P)
BRANCH=$(git branch --show-current)
  • GIT_DIR == GIT_COMMON: normal repo, no worktree cleanup needed
  • GIT_DIR != GIT_COMMON, named branch: worktree, provenance-based cleanup (Step 5)
  • Detached HEAD: reduced menu (no local merge), no cleanup (externally managed)

Step 3: Determine Base Branch

git merge-base HEAD main 2>/dev/null || git merge-base HEAD master 2>/dev/null

Or ask: "This branch split from main — correct?"

Step 4: Present Options

Normal repo / named-branch worktree — present exactly these 4 options:

Analysis complete and reproducible. What would you like to do?

1. Merge the analysis back to <base-branch> locally
2. Write up and share (report / preprint / pull request)
3. Keep the branch as-is (I'll handle it later)
4. Discard this work

Which option?

Detached HEAD — present these 3 (no local merge):

Analysis complete and reproducible (externally managed workspace).

1. Push as a new branch and open a pull request / share
2. Keep as-is
3. Discard this work

Which option?

Don't add explanation — keep options concise.

Step 5: Execute Choice

Option 1: Merge Locally

MAIN_ROOT=$(git -C "$(git rev-parse --git-common-dir)/.." rev-parse --show-toplevel)
cd "$MAIN_ROOT"
git checkout <base-branch> && git pull && git merge <feature-branch>
# Re-run the pipeline on the merged result; confirm it still reproduces

Then cleanup worktree (Step 6), then git branch -d <feature-branch>.

Option 2: Write Up and Share

Produce the report (see "Report Content" below). If sharing via PR:

git push -u origin <feature-branch>
gh pr create --title "<title>" --body "$(cat <<'EOF'
## Question
<the research question>

## What was done
<the pre-registered analysis, and any documented deviations>

## Findings
<confirmatory results with effect sizes + intervals>

## Exploratory (not confirmatory)
<clearly separated leads>

## Reproducibility
- Pre-registration: <path/commit>
- Environment: <lockfile>
- Seed: <value>
- Re-run: <command>
EOF
)"

Do NOT clean up the worktree — it's needed for iteration on feedback.

Option 3: Keep As-Is

Report: "Keeping branch <name>. Worktree preserved at <path>." No cleanup.

Option 4: Discard

Confirm first:

This will permanently delete:
- Branch <name>
- All commits: <list>
- Worktree at <path>

Type 'discard' to confirm.

Wait for the exact word. Then cleanup worktree (Step 6) and git branch -D <feature-branch>.

Step 6: Cleanup Workspace

Only for Options 1 and 4. Options 2 and 3 preserve the worktree.

WORKTREE_PATH=$(git rev-parse --show-toplevel)
  • GIT_DIR == GIT_COMMON: normal repo, nothing to clean up.
  • Worktree under .worktrees/, worktrees/, or ~/.config/superpowers/worktrees/: we own it.
    MAIN_ROOT=$(git -C "$(git rev-parse --git-common-dir)/.." rev-parse --show-toplevel)
    cd "$MAIN_ROOT"
    git worktree remove "$WORKTREE_PATH"
    git worktree prune
    
  • Otherwise: the harness owns it. Don't remove it.

Report Content

Every report MUST:

  • Separate confirmatory from exploratory. Confirmatory results are those that match the frozen pre-registration, run as registered. Everything else goes under an "Exploratory" heading and is described as a lead, not a conclusion.
  • Report effect sizes and intervals, not just p-values. Describe null results as inconclusive vs. evidence of absence honestly.
  • Document every deviation from the pre-registration and note that the affected analysis is exploratory.
  • State limitations and threats to validity carried from the design.
  • Avoid over-claiming — no causal language from observational data; no generalizing past the sample.

Archive for Reproducibility

Whatever the option, ensure the archive contains everything needed to regenerate the result:

  • The code (committed)
  • The environment lockfile and runtime version
  • The fixed seed
  • The frozen pre-registration (commit reference) and the passing prereg.sh audit output
  • Data provenance + checksums for raw inputs (and the data itself, or instructions to obtain it, per any data-sharing constraints)
  • The exact command(s) to re-run

Red Flags

Never:

  • Report a number you haven't just reproduced
  • Blur confirmatory and exploratory results
  • Report a p-value without an effect size and interval
  • Use causal language for an observational finding
  • Hide a deviation from the pre-registration
  • Remove a worktree before confirming a merge succeeded
  • Clean up a worktree you didn't create

Always:

  • Verify reproducibility before offering options
  • Present exactly 4 options (3 for detached HEAD)
  • Get typed confirmation for discard
  • Archive code + env + seed + pre-registration together

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/science-superpowers3512026年9月13日 更新

Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/science-superpowers3512026年9月13日 更新

Use when your human partner has explicitly opted into exploratory or feasibility mode - a compute-heavy simulation, an unproven pipeline, an unbenchmarked solver, an untested cluster job - or when whether the work can run at all is still unknown, when a plan's largest configuration has never been executed, when a memory or wall-clock ceiling is estimated rather than measured, or when someone proposes pre-registering over runs that have never happened

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/science-superpowers3512026年9月13日 更新

Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/science-superpowers3512026年9月13日 更新

You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/science-superpowers3512026年9月13日 更新

Use when a result is surprising, impossible, contradicts a sanity check, a pipeline fails, a model won't converge, or a replication fails - before adjusting anything

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/science-superpowers3512026年9月13日 更新

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